Internet memes are a powerful form of online communication, yet their multimodal nature and reliance on commonsense and cultural knowledge make automated toxicity detection challenging. Prior work has studied individual dimensions of memes: textual content via optical character recognition, visual content via object recognition, and toxicity via hate speech detection, but lacks a unified architecture that formally identifies the semantic dimensions contributing to meme toxicity. We present three contributions: (1) a semantic framework identifying the dimensions necessary for meme interpretation; (2) a corresponding benchmark, M-QUEST, of 609 question-answer pairs for 307 memes, generated via a semi-automatic process grounded in the framework; and (3) an empirical evaluation of 12 VLMs on M-QUEST. The results show that the instruction-tuned models with reasoning capabilities significantly outperform others, whereas pragmatic inference remains challenging across all models.

This paper faces the problem of the limited availability of datasets for Aspect-Based Sentiment Analysis (ABSA) in the Cultural Heritage domain. Currently, the main datasets for ABSA are product or restaurant reviews. We expand this to book reviews. Our methodology employs an LLM to maintain domain relevance while preserving the linguistic authenticity and natural variations found in genuine reviews. Entity types are annotated through the tool Text2AMR2FRED and evaluated manually. Additionally, we finetuned Llama 3.1 8B as a baseline model that not only performs ABSA, but also performs Entity Typing (ET) with a set of classes from DOLCE foundational ontology, enabling precise categorization of target aspects within book reviews. We present three key contributions as a step forward expanding ABSA: 1) a semi-synthetic set of book reviews, 2) an evaluation of Llama-3-1-Instruct 8B on the ABSA task, and 3) a fine-tuned version of Llama-3-1-Instruct 8B for ABSA.

Moral values as commonsense norms shape our everyday individual and community behavior. The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension. In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory. We develop and compare two different approaches: (i) a frame-based symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection. The final outcome from our work consists in two approaches meant to perform without the need for prior training process on a moral value detection task.